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README.md

@@ -1,15 +1,24 @@
 ---
 title: Phonetic Reduction
-emoji: 📉
-colorFrom: red
-colorTo: purple
+emoji: 🎙️
+colorFrom: blue
+colorTo: green
 sdk: gradio
 sdk_version: 6.14.0
-python_version: '3.13'
 app_file: app.py
 pinned: false
-license: cc-by-4.0
-short_description: Automatic score of phonetic reductions in a sentence
+license: mit
+short_description: Per-word phonetic reduction in Czech speech.
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Phonetic Reduction Analyzer (Czech)
+
+Per-word reduction scores via **STT → per-word TTS canonical reference → duration ratio + spectral DTW**.
+
+- Skóre `< 1` = slovo redukováno oproti průměru nahrávky
+- Skóre `> 1` = slovo plně artikulováno
+- Červené sloupce = nízká STT confidence (možný přeslech / halucinace)
+
+Pipeline: faster-whisper (large-v3) → silero-vad → piper-tts (cs_CZ-jirka-medium) → MFCC + DTW.
+
+Free CPU tier: ~30–60 s per ~10 s recording.

app.py

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+"""Gradio web UI for the phonetic-reduction analyzer.
+
+Run with:
+    .venv/bin/python app.py
+Then open the URL Gradio prints (defaults to http://127.0.0.1:7860).
+"""
+from __future__ import annotations
+
+import os
+import tempfile
+
+import gradio as gr
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import pandas as pd
+
+from reduction import pipeline, stt, tts
+
+
+# --- model cache ---------------------------------------------------------
+_models: dict[str, object] = {}
+_voice = None
+
+
+def _get_model(size: str):
+    if size not in _models:
+        _models[size] = stt.load_model(size=size)
+    return _models[size]
+
+
+def _get_voice():
+    global _voice
+    if _voice is None:
+        _voice = tts.load_voice()
+    return _voice
+
+
+# --- plot ----------------------------------------------------------------
+def _make_plot(df: pd.DataFrame) -> plt.Figure:
+    n = len(df)
+    fig, ax = plt.subplots(figsize=(max(8, n * 0.55), 4.5))
+    x = list(range(n))
+    labels = df["word"].tolist()
+
+    # Colour bars by STT confidence: low-confidence → red, normal → steelblue
+    probs = df["stt_probability"].fillna(1.0).tolist()
+    bar_colors = ["#cc3333" if p < 0.6 else "#4a90c4" for p in probs]
+    ax.bar(x, df["combined_score"], color=bar_colors, alpha=0.55, label="combined")
+    ax.plot(x, df["duration_score"], "o-", color="#2c7a2c", lw=1.4, label="duration", alpha=0.85)
+    ax.plot(x, df["spectral_score"], "s-", color="#d97726", lw=1.4, label="spectral", alpha=0.85)
+    ax.axhline(1.0, color="gray", linestyle="--", linewidth=0.8)
+
+    ax.set_xticks(x)
+    ax.set_xticklabels(labels, rotation=40, ha="right", fontsize=10)
+    ax.set_ylabel("skóre  (1 = průměr,  <1 redukováno)")
+    ax.set_xlabel("token")
+    ax.set_title("Per-word reduction scores")
+    ax.legend(loc="upper right", framealpha=0.9)
+    ax.grid(True, axis="y", alpha=0.25)
+    fig.tight_layout()
+    return fig
+
+
+# --- main analyze callback ----------------------------------------------
+def analyze_audio(audio_path: str | None, whisper_size: str, progress=gr.Progress()):
+    if not audio_path:
+        return "❌ Nahraj nebo nahrej zvuk.", None, None, None
+
+    progress(0.1, desc=f"Načítám Whisper {whisper_size}…")
+    model = _get_model(whisper_size)
+    progress(0.3, desc="Načítám Piper hlas…")
+    voice = _get_voice()
+
+    progress(0.5, desc="Analyzuji…")
+    rows, whisper_text, _ = pipeline.analyze(
+        audio_path, model, voice, language="cs", groundtruth=None, trim=True
+    )
+
+    progress(0.9, desc="Renderuji graf a CSV…")
+    df = pd.DataFrame(rows)
+
+    # Pretty display dataframe (round numerics, drop internal cols)
+    display_cols = [
+        "word",
+        "stt_probability",
+        "orig_duration",
+        "tts_duration",
+        "duration_ratio",
+        "spectral_distance",
+        "duration_score",
+        "spectral_score",
+        "combined_score",
+    ]
+    display_df = df[display_cols].copy()
+    for c in display_cols[1:]:
+        display_df[c] = display_df[c].round(3)
+
+    fig = _make_plot(df)
+
+    # CSV download (full row, not abbreviated)
+    csv_path = os.path.join(tempfile.gettempdir(), "reduction_result.csv")
+    df.to_csv(csv_path, index=False)
+
+    return whisper_text or "(žádný transkript)", display_df, fig, csv_path
+
+
+# --- UI -----------------------------------------------------------------
+HEADER_MD = """
+# Měření fonetické redukce — prototyp
+Per-word reduction scores via STT → per-word TTS canonical → duration + spectral DTW.
+Skóre **< 1** = redukováno oproti průměru nahrávky, **> 1** = plně artikulováno.
+Červené sloupce = nízká STT confidence (pravděpodobně přeslech / halucinace).
+"""
+
+with gr.Blocks(title="Phonetic reduction analyzer") as demo:
+    gr.Markdown(HEADER_MD)
+    with gr.Row():
+        with gr.Column(scale=2):
+            audio_in = gr.Audio(
+                sources=["upload", "microphone"],
+                type="filepath",
+                label="Nahrávka (drag & drop wav, nebo klikni na mikrofon a namluv)",
+            )
+        with gr.Column(scale=1):
+            whisper_dd = gr.Dropdown(
+                choices=["small", "medium", "large-v3"],
+                value="large-v3",
+                label="Whisper model",
+                info="large-v3 = nejlepší kvalita, ~6s/věta na CPU",
+            )
+            run_btn = gr.Button("Analyzuj", variant="primary", size="lg")
+
+    transcript_out = gr.Textbox(label="Whisper transkript", interactive=False, lines=2)
+    plot_out = gr.Plot(label="Skóre redukce")
+    table_out = gr.Dataframe(label="Per-word detaily", interactive=False)
+    csv_out = gr.File(label="Stáhnout CSV", interactive=False)
+
+    run_btn.click(
+        analyze_audio,
+        inputs=[audio_in, whisper_dd],
+        outputs=[transcript_out, table_out, plot_out, csv_out],
+    )
+
+
+if __name__ == "__main__":
+    # On HuggingFace Spaces, $SPACE_ID is set; on local dev it isn't.
+    on_hf = bool(os.environ.get("SPACE_ID"))
+    if on_hf:
+        # HF provides PORT and expects 0.0.0.0; let HF's launcher own model preloading
+        demo.launch(server_name="0.0.0.0")
+    else:
+        print("[startup] preloading large-v3 + Piper voice (one-time, ~10s)…")
+        _get_model("large-v3")
+        _get_voice()
+        print("[startup] ready, launching Gradio…")
+        demo.launch(server_name="127.0.0.1", server_port=7860, inbrowser=False)

requirements.txt

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+faster-whisper>=1.2
+piper-tts
+librosa>=0.11
+soundfile
+numpy
+silero-vad
+gradio>=6.0
+matplotlib
+pandas